1 citations · 1 across the 3 of their papers we have counts for
6 papers
Training Deep Capsule Networks with Residual Connections
Josef Gugglberger, David Peer, Antonio Rodriguez-Sanchez
Capsule networks are a type of neural network that have recently gained increased popularity. They consist of groups of neurons, called capsules, which encode properties of objects…
Auto-tuning of Deep Neural Networks by Conflicting Layer Removal
David Peer, Sebastian Stabinger, Antonio Rodriguez-Sanchez
Designing neural network architectures is a challenging task and knowing which specific layers of a model must be adapted to improve the performance is almost a mystery. In this pa…
Arguments for the Unsuitability of Convolutional Neural Networks for Non--Local Tasks
Sebastian Stabinger, David Peer, Antonio Rodríguez-Sánchez
Convolutional neural networks have established themselves over the past years as the state of the art method for image classification, and for many datasets, they even surpass huma…
Conflicting Bundles: Adapting Architectures Towards the Improved Training of Deep Neural Networks
David Peer, Sebastian Stabinger, Antonio Rodriguez-Sanchez
Designing neural network architectures is a challenging task and knowing which specific layers of a model must be adapted to improve the performance is almost a mystery. In this pa…
Limitation of capsule networks
David Peer, Sebastian Stabinger, Antonio Rodriguez-Sanchez
A recently proposed method in deep learning groups multiple neurons to capsules such that each capsule represents an object or part of an object. Routing algorithms route the outpu…
Increasing the adversarial robustness and explainability of capsule networks with -capsules
David Peer, Sebastian Stabinger, Antonio Rodriguez-Sanchez
In this paper we introduce a new inductive bias for capsule networks and call networks that use this prior -capsule networks. Our inductive bias that is inspired by TE neurons o…